333 Exemption

I am pleased to announce that AirGon’s request for amendment to its Section 333 waiver for flying commercial small Unmanned Aerial Systems (sUAS) was approved in April. Our amendment adds all current and future 333 approved aircraft to our 333. AirGon can now fly any sUAS that has ever been approved by the FAA as well as all future approved systems. This list currently contains 1,150 different sUAS (AirGon’s own AV-900 is number 207 on the list). This provides us a lot of flexibility in working with clients; for example, in situations where a glider sUAS is more efficient than a rotor craft.

The FAA has also recently streamlined the process of obtaining an N number for a sUAS. Prior to the change, a paper process that required several months was the only option. Now an online system is available, greatly simplifying this procedure. Note that this is not the new online registration system for hobby drones but rather the system used for obtaining an N number for a manned aircraft (if you are confused, join the club!). Combined with our new 333 amendment, we can now get a new aircraft legally operating within days.

We continue to do a lot of work to optimize the accuracy of point clouds derived from dense image matching (DIM). DIM are the data of choice for sUAS mapping since they can be generated from low cost prosumer cameras using standard application software such as Pix4D Mapper or PhotoScan. The question always remains as to how good these data really are.

It has taken us a lot of experimentation and analysis but we think we have fleshed out a procedure for assuring good absolute vertical accuracy. It involves the use of Real Time Kinematic (RTK) Global Navigation Satellite System (GNSS) positioning on the sUAS, a local base station that we tie into the national Continuously Operating Reference Station (CORS) network and the National Geodetic Survey’s Online Positioning User Service (OPUS) to “anchor” the project to the network. We have also discovered that high vertical accuracy cannot be obtained without camera calibration. We typically use an in situ process for calibration. We have flown many dozens of sites (primarily mining), giving us a rich set of test data.

I cannot over emphasize how critical network vertical accuracy is. Most customers want elevation maps of their sites. These are usually delivered as contour vector files. As we all know, a 1 foot contour requires vertical accuracy of 1/3 of a foot. This is a very tight requirement! A three inch vertical bias error over an acre is an error of about 400 cubic yards – this is significant.

We see a lot of drone companies processing site data with no control and no RTK/PPK. While, with the introduction of scale into the model (many companies do not even do this), one might obtain reasonable difference computations (such as volumes), the network accuracy is very poor (obtained from the airborne navigation grade GNSS only) and hence the data are of limited use. We have discovered that these techniques (where no control and/or RTK/PPK is used) can result in the vertical scale being incorrectly computed. This means that even differential measurements are not accurate. Why spend all of the money to collect these data if they are of unknown accuracy?

A more difficult area that we have studied over the past several years is what I refer to as “conformance.” That is, how well does the DIM actually fit the object being imaged? DIM processing software (again, such as Pix4D and PhotoScan) do a miraculous job correlating a 3D surface model from highly redundant imagery using the general class of algorithm called Structure from Motion (SfM). In addition to the obvious areas where SfM fails (deep shadow, thin linear objects such as poles and wires), a lot of subtle errors occur due to the filtering that is performed by the SfM post-extraction algorithms. These filtering algorithms are designed to remove noise from the surface model. Unfortunately, any filtering will also remove true signal, distorting the surface model.

We are working with several of our mining customers to quantify these errors and, once these errors are characterized, to develop best practices to minimize or at least recognize when and where they occur. An example of an analysis is shown in Figure 1. Here we are analyzing a small pile (roughly outlined in orange) of very coarse aggregates with a volume of about 340 cubic yards. This site was flown with a very high end manned aircraft LIDAR system and with AirGon’s AV-900 equipped with our RTK system. The DIM was created using Agisoft PhotoScan. We obtained excellent accuracy as determined by a number of signalized (meaning ground targets visible in the imagery) control and supplemental topo only shots. We used in situ calibration to calibrate the camera (a Sony NEX-5 with a 16 mm pancake lens).

As can be seen in Figure 1, we created a series of cross sections over the test pile. These cross sections were generated using the Cross Section Point Cloud Task (PCT) in LP360/Topolyst. This tool drapes cross sections at a user specified interval, conflating the elevation value from the user specified point cloud. We ran the task twice, conflating Z first from the LIDAR point cloud and then from the DIM. In Figure 1 we have drawn a profile over one of the cross sections with the result visible in the profile view. The red cross section is derived from the LIDAR and the green from the DIM.

Comparing LIDAR (red) to DIM (green)

Note that the DIM cross section (green) is considerably smoother than the LIDAR cross section (red). This is caused by several factors:

The aggregate of this particular pile is very coarse with some rocks over 2 feet in diameter. This leaves a very undulating surface. The LIDAR is fairly faithfully following this surface whereas the DIM is averaging over the surface.

The AV-900 flight was rather high and the data was collected with a 16 mm lens. This gave a ground sample distance (GSD) a little higher than is typical for this type project.

Due to the coarseness of the aggregate, significant pits appear between the rocks, creating deep shadows. SfM algorithms tend to blur in these regions, rendering the elevation less accurate than in areas of low shadow and good texture.

The impact of lower conformance is a function of both the material and the size of the stockpile (if stockpiles are what you are measuring). For small piles with very coarse material (as is the case in this example) a volumetric difference between LIDAR and SfM can be as great as 20%. On larger piles with finer aggregates, the conformance is significantly better. For example, in this same test project, we observed less than 0.25% difference between LIDAR and the DIM on a pile of #5 gravel containing about 30,000 cubic yards.

There still remains the question of which is more accurate – the volume as computed from the LIDAR or the volume as computed from the DIM? I think that if the LIDAR are collected with a post spacing ½ the diameter of the average rock, the LIDAR will be the most accurate (assuming that it is well calibrated and flown at very low altitude). However, the DIM is certainly sufficiently accurate for the vast majority of aggregate volumetric work, so long as a very strict adherence to collection and processing best practices is followed. For most high accuracy volumetric projects, manned LIDAR flights are prohibitively expensive.

We continue to do many experiments with local and network accuracy as well as methods to improve and quantify conformance. I’ll report our results here and in other articles as we continue to build our knowledge base.